影响因子：1.681
所属单位：数学与统计学院
教研室：统计教研室
发表刊物：Computational Statistics and Data Analysis
关键字：Case K interval-censored data, EM algorithm, Informative censoring, Sieve maximum likelihood estimation
摘要：The additive hazards model is one of the most commonly used model in regression analysis of failure time data and many estimation procedures have been developed for its inference under various situations (Kalbfleisch and Prentice (2002); Lin and Ying (1994); Sun (2006)). In this paper, we consider a situation, case K interval-censored data with informative interval censoring, that often occurs in practice such as medical follow-up studies but has not been discussed much in the literature due to the difficulties involved. For the problem, a joint model is proposed to describe the correlation between the failure time of interest and the underlying censoring or observation process and a sieve maximum likelihood approach is developed. In particular, an EM algorithm is presented for the implementation of the proposed estimation procedure and the asymptotic properties of the resulting estimators are established. A simulation study is conducted to assess the finite sample performance of the proposed method and suggests that it works well for practical situations. Also the method is applied to an AIDS study that motivated this study.
论文类型：期刊论文
卷号：144
期号：106891
是否译文：否
发表时间：2020-04-01
收录刊物：SCI